This is the repository for "PixMix: Dreamlike Pictures Comprehensively Improve Safety Measures"
by Dan Hendrycks*, Andy Zou*, Mantas Mazeika, Leonard Tang, Bo Li, Dawn Song, and Jacob Steinhardt.

In real-world applications of machine learning, reliable and safe systems must consider measures of performance beyond standard test set accuracy. These other goals include out-of-distribution (OOD) robustness, prediction consistency, resilience to adversaries, calibrated uncertainty estimates, and the ability to detect anomalous inputs. However, improving performance towards these goals is often a balancing act that today’s methods cannot achieve without sacrificing performance on other safety axes, raising the question of whether a Pareto improvement on all existing safety measures is possible. To meet this challenge, we design a new data augmentation strategy utilizing the natural structural complexity of pictures such as fractals, which outperforms numerous baselines, is near Pareto-optimal, and comprehensively improves safety measures.

pixmix_utils.py includes reference implementation of augmentations and mixings used in PixMix.

We also include PyTorch implementations of PixMix on both CIFAR-10/100 and ImageNet in cifar.py and imagenet.py respectively, which both support training and evaluation on CIFAR-10/100-C and ImageNet-C/R.

Training recipes used in our paper:

CIFAR:

python cifar.py \
  --dataset <cifar10 or cifar100> \
  --data-path <path/to/cifar and cifar-c> \
  --mixing-set <path/to/mixing_set> \
  --all-ops

ImageNet 1K:

python imagenet.py \
  --data-standard <path/to/imagenet_train> \
  --data-val <path/to/imagenet_val> \
  --imagenet-r-dir <path/to/imagenet_r> \
  --imagenet-c-dir <path/to/imagenet_c> \
  --mixing-set <path/to/mixing_set> \
  --num-classes 1000 \
  --all-ops

The mixing set of fractals and feature visualizations used in the paper can be downloaded here.

Weights for a 40x4-WRN CIFAR-10/100 classifier trained with PixMix for 100 epochs are available here.

Weights for a ResNet-50 ImageNet classifier trained with PixMix for 90 and 180 epochs are available here.

If you find this useful in your research, please consider citing:

@article{hendrycks2022robustness,
  title={PixMix: Dreamlike Pictures Comprehensively Improve Safety Measures},
  author={Dan Hendrycks and Andy Zou and Mantas Mazeika and Leonard Tang and Bo Li and Dawn Song and Jacob Steinhardt},
  journal={CVPR},
  year={2022}
}